Scalable and efficient clustering for fingerprint-based positioning
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/85318 |
Resumo: | Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by approximate to 7% with respect to fingerprinting with the traditional clustering models. |
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Scalable and efficient clustering for fingerprint-based positioningClustering algorithmsWireless fidelityComputational modelingInternet of ThingsEstimationFingerprint recognitionReceiversk-meansBluetooth low energy (BLE)received signal strength (RSS)Wi-Fiaffinity propagationclusteringfingerprintingindoor localizationEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaScience & TechnologyIndoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by approximate to 7% with respect to fingerprinting with the traditional clustering models.This work was supported by the European Union's H2020 Research and Innovation Programme under the Marie Sklodowska-Curie under Agreement 813278 (A-WEAR, http://www.a-wear.eu/) and Agreement 101023072 (ORIENTATE,http://orientate.dsi.uminho.pt).IEEEUniversidade do MinhoTorres-Sospedra, JoaquínQuezada Gaibor, Darwin P.Nurmi, JariKoucheryavy, YevgeniLohan, Elena SimonaHuerta, Joaquin2023-02-152023-02-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/85318engJ. Torres-Sospedra, D. P. Quezada Gaibor, J. Nurmi, Y. Koucheryavy, E. S. Lohan and J. Huerta, "Scalable and Efficient Clustering for Fingerprint-Based Positioning," in IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3484-3499, 15 Feb.15, 2023, doi: 10.1109/JIOT.2022.3230913.2327-466210.1109/JIOT.2022.3230913https://ieeexplore.ieee.org/document/9993735info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:46:21Zoai:repositorium.sdum.uminho.pt:1822/85318Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:44:20.440816Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Scalable and efficient clustering for fingerprint-based positioning |
title |
Scalable and efficient clustering for fingerprint-based positioning |
spellingShingle |
Scalable and efficient clustering for fingerprint-based positioning Torres-Sospedra, Joaquín Clustering algorithms Wireless fidelity Computational modeling Internet of Things Estimation Fingerprint recognition Receivers k-means Bluetooth low energy (BLE) received signal strength (RSS) Wi-Fi affinity propagation clustering fingerprinting indoor localization Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
title_short |
Scalable and efficient clustering for fingerprint-based positioning |
title_full |
Scalable and efficient clustering for fingerprint-based positioning |
title_fullStr |
Scalable and efficient clustering for fingerprint-based positioning |
title_full_unstemmed |
Scalable and efficient clustering for fingerprint-based positioning |
title_sort |
Scalable and efficient clustering for fingerprint-based positioning |
author |
Torres-Sospedra, Joaquín |
author_facet |
Torres-Sospedra, Joaquín Quezada Gaibor, Darwin P. Nurmi, Jari Koucheryavy, Yevgeni Lohan, Elena Simona Huerta, Joaquin |
author_role |
author |
author2 |
Quezada Gaibor, Darwin P. Nurmi, Jari Koucheryavy, Yevgeni Lohan, Elena Simona Huerta, Joaquin |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Torres-Sospedra, Joaquín Quezada Gaibor, Darwin P. Nurmi, Jari Koucheryavy, Yevgeni Lohan, Elena Simona Huerta, Joaquin |
dc.subject.por.fl_str_mv |
Clustering algorithms Wireless fidelity Computational modeling Internet of Things Estimation Fingerprint recognition Receivers k-means Bluetooth low energy (BLE) received signal strength (RSS) Wi-Fi affinity propagation clustering fingerprinting indoor localization Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
topic |
Clustering algorithms Wireless fidelity Computational modeling Internet of Things Estimation Fingerprint recognition Receivers k-means Bluetooth low energy (BLE) received signal strength (RSS) Wi-Fi affinity propagation clustering fingerprinting indoor localization Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática Science & Technology |
description |
Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by approximate to 7% with respect to fingerprinting with the traditional clustering models. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-02-15 2023-02-15T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/85318 |
url |
https://hdl.handle.net/1822/85318 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
J. Torres-Sospedra, D. P. Quezada Gaibor, J. Nurmi, Y. Koucheryavy, E. S. Lohan and J. Huerta, "Scalable and Efficient Clustering for Fingerprint-Based Positioning," in IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3484-3499, 15 Feb.15, 2023, doi: 10.1109/JIOT.2022.3230913. 2327-4662 10.1109/JIOT.2022.3230913 https://ieeexplore.ieee.org/document/9993735 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
dc.source.none.fl_str_mv |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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